QWen-VL-Chat1.0 / tokenization_qwen.py

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	# Copyright (c) Alibaba Cloud.
	#
	# This source code is licensed under the license found in the
	# LICENSE file in the root directory of this source tree.

	"""Tokenization classes for QWen."""

	import base64
	import logging
	import os
	import requests
	import unicodedata
	from typing import Collection, Dict, List, Set, Tuple, Union, Any, Callable, Optional

	import tiktoken
	import numpy as np
	from PIL import Image
	from PIL import ImageFont
	from PIL import ImageDraw
	from transformers import PreTrainedTokenizer, AddedToken
	from transformers.utils import try_to_load_from_cache

	import matplotlib.colors as mcolors
	from matplotlib.font_manager import FontProperties

	logger = logging.getLogger(__name__)


	VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken", "ttf": "SimSun.ttf"}
	FONT_PATH = try_to_load_from_cache("Qwen/Qwen-VL-Chat", "SimSun.ttf")
	if FONT_PATH is None:
	if not os.path.exists("SimSun.ttf"):
	ttf = requests.get("https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/SimSun.ttf")
	open("SimSun.ttf", "wb").write(ttf.content)
	FONT_PATH = "SimSun.ttf"

	PAT_STR = r"""(?i:'s\|'t\|'re\|'ve\|'m\|'ll\|'d)\|[^\r\n\p{L}\p{N}]?\p{L}+\|\p{N}\| ?[^\s\p{L}\p{N}]+[\r\n]\|\s[\r\n]+\|\s+(?!\S)\|\s+"""
	ENDOFTEXT = "<\|endoftext\|>"
	IMSTART = "<\|im_start\|>"
	IMEND = "<\|im_end\|>"
	# as the default behavior is changed to allow special tokens in
	# regular texts, the surface forms of special tokens need to be
	# as different as possible to minimize the impact
	EXTRAS = tuple((f"<\|extra_{i}\|>" for i in range(205)))
	SPECIAL_TOKENS = (
	ENDOFTEXT,
	IMSTART,
	IMEND,
	) + EXTRAS
	IMG_TOKEN_SPAN = 256


	def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
	with open(tiktoken_bpe_file, "rb") as f:
	contents = f.read()
	return {
	base64.b64decode(token): int(rank)
	for token, rank in (line.split() for line in contents.splitlines() if line)
	}

	def _list_find(
	input_list: List[Any],
	candidates: Tuple[Any],
	start: int = 0,
	):
	for i in range(start, len(input_list)):
	if input_list[i] in candidates:
	return i
	return -1

	def _replace_closed_tag(
	input_tokens: List[Any],
	start_tags: Union[Any, Tuple[Any]],
	end_tags: Union[Any, Tuple[Any]],
	inclusive_replace_func: Callable,
	exclusive_replace_func: Callable = lambda x: x,
	):
	if isinstance(start_tags, (str, int)):
	start_tags = (start_tags,)
	if isinstance(end_tags, (str, int)):
	end_tags = (end_tags,)
	assert len(start_tags) == len(end_tags)

	output_tokens = []
	end = 0
	while True:
	start = _list_find(input_tokens, start_tags, end)
	if start == -1:
	break
	output_tokens.extend(exclusive_replace_func(input_tokens[end : start]))
	tag_idx = start_tags.index(input_tokens[start])
	end = _list_find(input_tokens, (end_tags[tag_idx],), start)
	if end == -1:
	raise ValueError("Unclosed image token")
	output_tokens.extend(inclusive_replace_func(input_tokens[start : end + 1]))
	end += 1
	output_tokens.extend(exclusive_replace_func(input_tokens[end : ]))
	return output_tokens

	class QWenTokenizer(PreTrainedTokenizer):
	"""QWen tokenizer."""

	vocab_files_names = VOCAB_FILES_NAMES

	def __init__(
	self,
	vocab_file,
	errors="replace",
	image_start_tag='<img>',
	image_end_tag='</img>',
	image_pad_tag='<imgpad>',
	ref_start_tag='<ref>',
	ref_end_tag='</ref>',
	box_start_tag='<box>',
	box_end_tag='</box>',
	quad_start_tag='<quad>',
	quad_end_tag='</quad>',
	**kwargs,
	):
	super().__init__(**kwargs)
	self.image_start_tag = image_start_tag
	self.image_end_tag = image_end_tag
	self.image_pad_tag = image_pad_tag
	self.ref_start_tag = ref_start_tag
	self.ref_end_tag = ref_end_tag
	self.box_start_tag = box_start_tag
	self.box_end_tag = box_end_tag
	self.quad_start_tag = quad_start_tag
	self.quad_end_tag = quad_end_tag
	self.IMAGE_ST = (
	ref_start_tag, ref_end_tag,
	box_start_tag, box_end_tag,
	quad_start_tag, quad_end_tag,
	image_start_tag, image_end_tag,
	image_pad_tag
	)

	self.errors = errors # how to handle errors in decoding

	self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: dict[bytes, int]
	self.special_tokens = {
	token: index
	for index, token in enumerate(
	SPECIAL_TOKENS + self.IMAGE_ST, start=len(self.mergeable_ranks)
	)
	}
	self.img_start_id = self.special_tokens[self.image_start_tag]
	self.img_end_id = self.special_tokens[self.image_end_tag]
	self.img_pad_id = self.special_tokens[self.image_pad_tag]
	self.ref_start_id = self.special_tokens[self.ref_start_tag]
	self.ref_end_id = self.special_tokens[self.ref_end_tag]
	self.box_start_id = self.special_tokens[self.box_start_tag]
	self.box_end_id = self.special_tokens[self.box_end_tag]
	self.quad_start_id = self.special_tokens[self.quad_start_tag]
	self.quad_end_id = self.special_tokens[self.quad_end_tag]

	enc = tiktoken.Encoding(
	"Qwen",
	pat_str=PAT_STR,
	mergeable_ranks=self.mergeable_ranks,
	special_tokens=self.special_tokens,
	)
	assert (
	len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
	), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"

	self.decoder = {
	v: k for k, v in self.mergeable_ranks.items()
	} # type: dict[int, bytes\|str]
	self.decoder.update({v: k for k, v in self.special_tokens.items()})

	self.tokenizer = enc # type: tiktoken.Encoding

	self.eod_id = self.tokenizer.eot_token
	self.im_start_id = self.special_tokens[IMSTART]
	self.im_end_id = self.special_tokens[IMEND]

	def __getstate__(self):
	# for pickle lovers
	state = self.__dict__.copy()
	del state['tokenizer']
	return state

	def __setstate__(self, state):
	# tokenizer is not python native; don't pass it; rebuild it
	self.__dict__.update(state)
	enc = tiktoken.Encoding(
	"Qwen",
	pat_str=PAT_STR,
	mergeable_ranks=self.mergeable_ranks,
	special_tokens=self.special_tokens,
	)
	self.tokenizer = enc


	def __len__(self) -> int:
	return self.tokenizer.n_vocab

	def get_vocab(self) -> Dict[bytes, int]:
	return self.mergeable_ranks

	def convert_tokens_to_ids(
	self, tokens: Union[bytes, str, List[Union[bytes, str]]]
	) -> List[int]:
	ids = []
	if isinstance(tokens, (str, bytes)):
	if tokens in self.special_tokens:
	return self.special_tokens[tokens]
	else:
	return self.mergeable_ranks.get(tokens)
	for token in tokens:
	if token in self.special_tokens:
	ids.append(self.special_tokens[token])
	else:
	ids.append(self.mergeable_ranks.get(token))
	return ids

	def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int:
	if not special_tokens and new_tokens:
	raise ValueError('Adding regular tokens is not supported')
	for token in new_tokens:
	surface_form = token.content if isinstance(token, AddedToken) else token
	if surface_form not in SPECIAL_TOKENS + self.IMAGE_ST:
	raise ValueError('Adding unknown special tokens is not supported')
	return 0

	def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
	"""
	Save only the vocabulary of the tokenizer (vocabulary).

	Returns:
	`Tuple(str)`: Paths to the files saved.
	"""
	file_path = os.path.join(save_directory, "qwen.tiktoken")
	with open(file_path, "w", encoding="utf8") as w:
	for k, v in self.mergeable_ranks.items():
	line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
	w.write(line)
	return (file_path,)

	def tokenize(
	self,
	text: str,
	allowed_special: Union[Set, str] = "all",
	disallowed_special: Union[Collection, str] = (),
	**kwargs,
	) -> List[Union[bytes, str]]:
	"""
	Converts a string in a sequence of tokens.

	Args:
	text (`str`):
	The sequence to be encoded.
	allowed_special (`Literal["all"]` or `set`):
	The surface forms of the tokens to be encoded as special tokens in regular texts.
	Default to "all".
	disallowed_special (`Literal["all"]` or `Collection`):
	The surface forms of the tokens that should not be in regular texts and trigger errors.
	Default to an empty tuple.

	kwargs (additional keyword arguments, optional):
	Will be passed to the underlying model specific encode method.

	Returns:
	`List[bytes\|str]`: The list of tokens.
	"""
	tokens = []
	text = unicodedata.normalize("NFC", text)

	# this implementation takes a detour: text -> token id -> token surface forms
	for t in self.tokenizer.encode(
	text, allowed_special=allowed_special, disallowed_special=disallowed_special
	):
	tokens.append(self.decoder[t])

	def _encode_imgurl(img_tokens):
	assert img_tokens[0] == self.image_start_tag and img_tokens[-1] == self.image_end_tag
	img_tokens = img_tokens[1:-1]
	img_url = b''.join(img_tokens)
	out_img_tokens = list(map(self.decoder.get, img_url))
	if len(out_img_tokens) > IMG_TOKEN_SPAN:
	raise ValueError("The content in {}..{} is too long".format(
	self.image_start_tag, self.image_end_tag))
	out_img_tokens.extend([self.image_pad_tag] * (IMG_TOKEN_SPAN - len(out_img_tokens)))
	out_img_tokens = [self.image_start_tag] + out_img_tokens + [self.image_end_tag]
	return out_img_tokens

	return _replace_closed_tag(tokens, self.image_start_tag, self.image_end_tag, _encode_imgurl)

	def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
	"""
	Converts a sequence of tokens in a single string.
	"""
	text = ""
	temp = b""
	for t in tokens:
	if isinstance(t, str):
	if temp:
	text += temp.decode("utf-8", errors=self.errors)
	temp = b""
	text += t
	elif isinstance(t, bytes):
	temp += t
	else:
	raise TypeError("token should only be of type types or str")
	if temp:
	text += temp.decode("utf-8", errors=self.errors)
	return text

	@property
	def vocab_size(self):
	return self.tokenizer.n_vocab

	def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
	"""Converts an id to a token, special tokens included"""
	if index in self.decoder:
	return self.decoder[index]
	raise ValueError("unknown ids")

	def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
	"""Converts a token to an id using the vocab, special tokens included"""
	if token in self.special_tokens:
	return self.special_tokens[token]
	if token in self.mergeable_ranks:
	return self.mergeable_ranks[token]
	raise ValueError("unknown token")

	def _tokenize(self, text: str, **kwargs):
	"""
	Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
	vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).

	Do NOT take care of added tokens.
	"""
	raise NotImplementedError

	def _decode(
	self,
	token_ids: Union[int, List[int]],
	skip_special_tokens: bool = False,
	errors: str = None,
	**kwargs,
	) -> str:
	if isinstance(token_ids, int):
	token_ids = [token_ids]

	def _decode_imgurl(img_token_ids):
	assert img_token_ids[0] == self.img_start_id and img_token_ids[-1] == self.img_end_id
	img_token_ids = img_token_ids[1:-1]
	img_token_ids = img_token_ids[ : img_token_ids.index(self.img_pad_id)]
	img_url = bytes(img_token_ids).decode('utf-8')
	return [self.img_start_id] + self.tokenizer.encode(img_url) + [self.img_end_id]

	token_ids = _replace_closed_tag(token_ids, self.img_start_id, self.img_end_id, _decode_imgurl)

	if skip_special_tokens:
	token_ids = [i for i in token_ids if i < self.eod_id]
	return self.tokenizer.decode(token_ids, errors=errors or self.errors)

	def to_list_format(self, text: str):
	text = unicodedata.normalize("NFC", text)
	token_ids = self.tokenizer.encode(
	text, allowed_special=set(self.IMAGE_ST + (ENDOFTEXT,)))

	def _encode_vl_info(tokens):
	if len(tokens) == 0:
	return []
	if tokens[0] == self.img_start_id and tokens[-1] == self.img_end_id:
	key = 'image'
	elif tokens[0] == self.ref_start_id and tokens[-1] == self.ref_end_id:
	key = 'ref'
	elif tokens[0] == self.box_start_id and tokens[-1] == self.box_end_id:
	key = 'box'
	elif tokens[0] == self.quad_start_id and tokens[-1] == self.quad_end_id:
	key = 'quad'
	else:
	_tobytes = lambda x: x.encode('utf-8') if isinstance(x, str) else x
	return [{'text': b''.join(map(_tobytes, map(self.decoder.get, tokens))).decode('utf-8')}]
	_tobytes = lambda x: x.encode('utf-8') if isinstance(x, str) else x
	val = b''.join(map(_tobytes, map(self.decoder.get, tokens[1:-1]))).decode('utf-8')
	return [{key: val}]

	return _replace_closed_tag(
	token_ids,
	(self.img_start_id, self.ref_start_id, self.box_start_id, self.quad_start_id),
	(self.img_end_id, self.ref_end_id, self.box_end_id, self.quad_end_id),
	_encode_vl_info,
	_encode_vl_info,
	)

	def from_list_format(self, list_format: List[Dict]):
	text = ''
	num_images = 0
	for ele in list_format:
	if 'image' in ele:
	num_images += 1
	text += f'Picture {num_images}:'
	text += self.image_start_tag + ele['image'] + self.image_end_tag
	text += '\n'
	elif 'text' in ele:
	text += ele['text']
	elif 'box' in ele:
	if 'ref' in ele:
	text += self.ref_start_tag + ele['ref'] + self.ref_end_tag
	for box in ele['box']:
	text += self.box_start_tag + '(%d,%d),(%d,%d)' % (box[0], box[1], box[2], box[3]) + self.box_end_tag
	else:
	raise ValueError("Unsupport element: " + str(ele))
	return text

	def _fetch_latest_picture(self, response, history):
	if history is None:
	history = []
	_history = history + [(response, None)]
	for q, r in _history[::-1]:
	for ele in self.to_list_format(q)[::-1]:
	if 'image' in ele:
	return ele['image']
	return None

	def _fetch_all_box_with_ref(self, text):
	list_format = self.to_list_format(text)
	output = []
	for i, ele in enumerate(list_format):
	if 'box' in ele:
	bbox = tuple(map(int, ele['box'].replace('(', '').replace(')', '').split(',')))
	assert len(bbox) == 4
	output.append({'box': bbox})
	if i > 0 and 'ref' in list_format[i-1]:
	output[-1]['ref'] = list_format[i-1]['ref'].strip()
	return output

	def draw_bbox_on_latest_picture(
	self,
	response,
	history=None,
	) -> Optional[Image.Image]:
	image = self._fetch_latest_picture(response, history)
	if image is None:
	return None
	if image.startswith("http://") or image.startswith("https://"):
	image = Image.open(requests.get(image, stream=True).raw).convert("RGB")
	h, w = image.height, image.width
	else:
	image = np.asarray(Image.open(image).convert("RGB"))
	h, w = image.shape[0], image.shape[1]
	visualizer = Visualizer(image)

	boxes = self._fetch_all_box_with_ref(response)
	if not boxes:
	return None
	color = random.choice([_ for _ in mcolors.TABLEAU_COLORS.keys()]) # init color
	for box in boxes:
	if 'ref' in box: # random new color for new refexps
	color = random.choice([_ for _ in mcolors.TABLEAU_COLORS.keys()])
	x1, y1, x2, y2 = box['box']
	x1, y1, x2, y2 = (int(x1 / 1000 * w), int(y1 / 1000 * h), int(x2 / 1000 * w), int(y2 / 1000 * h))
	visualizer.draw_box((x1, y1, x2, y2), alpha=1, edge_color=color)
	if 'ref' in box:
	visualizer.draw_text(box['ref'], (x1, y1), color=color, horizontal_alignment="left")
	return visualizer.output


	import colorsys
	import logging
	import math
	import numpy as np
	import matplotlib as mpl
	import matplotlib.colors as mplc
	import matplotlib.figure as mplfigure
	import torch
	from matplotlib.backends.backend_agg import FigureCanvasAgg
	from PIL import Image
	import random

	logger = logging.getLogger(__name__)


	class VisImage:
	def __init__(self, img, scale=1.0):
	self.img = img
	self.scale = scale
	self.width, self.height = img.shape[1], img.shape[0]
	self._setup_figure(img)

	def _setup_figure(self, img):
	fig = mplfigure.Figure(frameon=False)
	self.dpi = fig.get_dpi()
	# add a small 1e-2 to avoid precision lost due to matplotlib's truncation
	# (https://github.com/matplotlib/matplotlib/issues/15363)
	fig.set_size_inches(
	(self.width * self.scale + 1e-2) / self.dpi,
	(self.height * self.scale + 1e-2) / self.dpi,
	)
	self.canvas = FigureCanvasAgg(fig)
	# self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)
	ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])
	ax.axis("off")
	self.fig = fig
	self.ax = ax
	self.reset_image(img)

	def reset_image(self, img):
	img = img.astype("uint8")
	self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation="nearest")

	def save(self, filepath):
	self.fig.savefig(filepath)

	def get_image(self):
	canvas = self.canvas
	s, (width, height) = canvas.print_to_buffer()

	buffer = np.frombuffer(s, dtype="uint8")

	img_rgba = buffer.reshape(height, width, 4)
	rgb, alpha = np.split(img_rgba, [3], axis=2)
	return rgb.astype("uint8")


	class Visualizer:
	def __init__(self, img_rgb, metadata=None, scale=1.0):
	self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)
	self.font_path = FONT_PATH
	self.output = VisImage(self.img, scale=scale)
	self.cpu_device = torch.device("cpu")

	# too small texts are useless, therefore clamp to 14
	self._default_font_size = max(
	np.sqrt(self.output.height * self.output.width) // 30, 15 // scale
	)

	def draw_text(
	self,
	text,
	position,
	*,
	font_size=None,
	color="g",
	horizontal_alignment="center",
	rotation=0,
	):
	if not font_size:
	font_size = self._default_font_size

	# since the text background is dark, we don't want the text to be dark
	color = np.maximum(list(mplc.to_rgb(color)), 0.2)
	color[np.argmax(color)] = max(0.8, np.max(color))

	x, y = position
	self.output.ax.text(
	x,
	y,
	text,
	size=font_size * self.output.scale,
	fontproperties=FontProperties(fname=self.font_path),
	bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"},
	verticalalignment="top",
	horizontalalignment=horizontal_alignment,
	color=color,
	zorder=10,
	rotation=rotation,
	)
	return self.output

	def draw_box(self, box_coord, alpha=0.5, edge_color="g", line_style="-"):

	x0, y0, x1, y1 = box_coord
	width = x1 - x0
	height = y1 - y0

	linewidth = max(self._default_font_size / 4, 1)

	self.output.ax.add_patch(
	mpl.patches.Rectangle(
	(x0, y0),
	width,
	height,
	fill=False,
	edgecolor=edge_color,
	linewidth=linewidth * self.output.scale,
	alpha=alpha,
	linestyle=line_style,
	)
	)
	return self.output

	def get_output(self):

	return self.output